Bingham Farms, MI (PRWEB) June 18, 2013
Big Data can in practice be considered: Structured data greater than 20 TB; or Unstructured data greater than 1 TB; or Data acquired from outside sources in large volumes.
Big data is everyone’s data today. Big data used to be only for large corporations that needed to handle large amounts of structured commercial data. This is not current reality. Big data is now in practice defined as: Structured data greater than 20 TB; or Unstructured data greater than 1 TB; or a combination of these when speaking strictly in terms of size. Large volumes and high input/processing speed requirements are also characteristic of Big Data.
But speaking in terms of group or class, Big Data is often referred to also as data that is not generated in-house, but is acquired and utilized for purposes such as analytics and business intelligence. Consider the following example: An insurance company releases a customized version of a policy for teenage drivers. An agent previews the sales statistics and realizes he has not met the quota of expected sales. They are definitely in the need to make a decision whether to keep carrying the policy or not. Often times, a totally wrong decision is made by dropping the program based on in-house data statistics only. In this case, Big Data can be brought in to make a better-informed decision and solve the problem faced by the agency. The agency can acquire sales statistics for that type of policy from around the state as well as from around the country. Either of the data sets could provide proper analytics with regard to the sales statistics of the particular policy across the state and across the country and would allow the agency to make the right decision. Assuming the policy sells well elsewhere within the state, the agency can take a certain course of action to adjust their sales methodologies. However in the case of the policy not selling well within the state but doing well around the country, the agency can take a different route to fine tune their marketing strategy as well as modify the policy structure in conjunction with their underwriters. In either case they are able to make the proper decision to keep carrying the program by addressing the deficiencies in both situations. This example illustrates how Big Data Analytics is brought to bear in even relatively small/average business environments. However, Big Data analytics requires the right hardware and ideally the right NoSQL type DBMS to handle the loads with high storage efficiency and speed. GENSONIX